Cross-Task Fine-Tuning for Robust CWE Detection in Python Code Under Adversarial Perturbations

Abstract

Abstract: This report synthesises findings from 4 peer-reviewed papers addressing the following research question: How does domain adaptation via cross-task fine-tuning affect the robustness of SLMs in detecting CWEs in Python code under adversarial perturbations compared to a baseline of pre-trained LLMs. A joint measurement is presented of the branching fractions \$B0\_sto\$mu\$+\$mu\$-\$ and \$B0to\$mu\$+\$mu\$-\$ in proton-proton collisions at the LHC by the CMS and LHCb experiments. The data samples were collected in 2011 at a centre-of-mass energy of 7 TeV, and in 2012 at 8 TeV. 6 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 0.3/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does domain adaptation via cross-task fine-tuning affect the robustness of SLMs in detecting CWEs in Python code under adversarial perturbations compared to a baseline of pre-trained LLMs?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance